1. Technical Field
This disclosure relates to digital image processing and more particularly to a method of chromatic classification of pixels and a method of adaptive enhancement of a color image that uses the method of chromatic classification.
2. Description of the Related Art
The diffusion of cameras, cellular phones and other personal equipment capable of taking pictures is a thrust to devise techniques for enhancing images both in global and semantic terms. See S. Battiato, A. Castorina, M. Guarnera, P. Vivirito, “A Global Enhancement Pipeline for Low-cost Imaging Devices,” IEEE Transactions on Consumer Electronics, Vol. 49, Issue 3, pp. 670-675 (August 2003), and S. Battiato, A. Bosco, A. Castorina, G. Messina, “Automatic Global Image Enhancement by Skin Dependent Exposure Correction,” Proc. of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing—NSIP 2003, Grado, Italy (June 2003).
For pictures of natural scenes, panoramas, portraits and the like, it is normally assumed that human eyes are more sensitive to colors of a restricted number of classes. See S. N. Yendrikhovskij, F. J. J. Blommaert, H. de Ridder, “Optimizing color reproduction of natural images”, Proc. Of Sixth Color Imaging Conference: Color Science, Systems, and Applications, pp. 140-145 (1998); E. J. Lee, Y. H. Ha, “Favorite Color Correction for Favorite Colors”, IEEE Trans. On Consumer Electronics, vol. 44, No. 1, pp. 10-15 (February 1998); Do-Hun Kim, Hyun-Chul Do, Sung-II Chien, “Preferred Skin Color Reproduction Based on Adaptive Affine Transform”, IEEE Transactions on Consumer Electronics, 192 Vol. 51, No. 1 (February 2005); Eung-Joo Lee, Yeong-Ho Ha, “Automatic Flesh Tone Reappearance for Color Enhancement in TV,” IEEE Trans. on Consumer Electronics, Vol. 43, No. 4, pp. 1153-1159 (November, 1997); U.S. Pat. No. 6,738,510, “Image Processing Apparatus” (2001).
Studies have demonstrated that the chromatic classes are to which human eyes most sensitive are: complexion or skin, vegetation, sky/sea.
These classes appear to have the most intense impact on the human visual system. Classic global techniques (histogram equalization, contrast enhancing) work without supervision, that is without considering specific peculiarities of the color classes of a picture to be processed. See R. C. Gonzalez, R. E. Woods, “Digital Image Processing”, 2nd edition, Prentice Hall (2002).
Different solutions known in the art contemplate a color correction. For example (PAINTSHOPPRO software from Jasc Software Inc., www_dot_jasc_dot_com; (CORRECT software from Pictographics Inc., www_dot_picto_dot_com) produce an automatic color enhancement. The first of the two referenced techniques carries out automatic saturation enhancement by correcting the whole image in a same way without implementing any adaptive control. The second technique carries out a manually guided color correction, thus in a semiautomatic mode. Users must indicate target colors for real classes and a global correction on the whole image is carried out, often generating unpleasant color merging artifacts. In both cases, a global correction is carried out on the whole image.
The U.S. Pat. No. 6,738,510 discloses a guided correction procedure based on the color classes of an image.
Other works, such as U.S. Pat. No. 6,721,000, “Adaptive Pixel-level color enhancement for a digital camera” (Apr. 13, 2004), U.S. Pat. No. 6,611,618, “Wide-Band Image Enhancement” (Aug. 26, 2003) and D. Comaniciu, D. Meer, “Robust Analysis of Feature Spaces: Color Image Segmentation”, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 750-755 (1999), disclose color correction techniques in a general or adaptive fashion.
Techniques of image correction/enhancement according to an adaptive approach, even if potentially very effective, have not gained diffused application because of intrinsic difficulties in adequately filtering the large amount of statistical data retrievable from an image to be processed that may include many details of different colors, without requiring an expensively large amount of computational resources.